Summary of Implicit Reasoning in Deep Time Series Forecasting, by Willa Potosnak et al.
Implicit Reasoning in Deep Time Series Forecasting
by Willa Potosnak, Cristian Challu, Mononito Goswami, Michał Wiliński, Nina Żukowska, Artur Dubrawski
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates whether state-of-the-art time series foundation models’ zero-shot forecasting performance is due to their understanding of temporal dynamics or simply memorizing the training data. By assessing the reasoning abilities of these models in systematically orchestrated out-of-distribution scenarios, the authors find that certain models generalize effectively, suggesting underexplored reasoning capabilities beyond pattern memorization. The study uses linear, MLP-based, and patch-based Transformer models to explore the limits of their temporal reasoning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well AI models can predict future events in time series data without being trained for that specific task. The authors tested different types of models and found that some are able to make good predictions even when they’re given new information that’s a bit different from what they were trained on. This shows that these models might be more clever than just memorizing patterns, but it’s still not fully understood how well they can reason about time series data. |
Keywords
» Artificial intelligence » Time series » Transformer » Zero shot
